A common task encountered in the field of signal processing is the sampling and processing of a physical state using multiple, ideally independent, signal sensors. The diversity of the resulting multi-sensor or multi-channel signal typically reveals more information about the underlying sampled state than can be obtained from employing a single sensor.
Multi-channel signal processing is utilized in biomedical applications. For example, in the field of neurological monitoring for epileptic seizure detection or prediction, multiple electrodes may be implanted in diverse locations on or in a patient's brain to monitor the susceptibility of the patient to enter into an epileptic seizure. The multi-channel signals generated by the electrodes may be processed to, e.g., alert the patient and/or medical personnel of a high likelihood of imminent seizure. See, e.g., commonly-owned U.S. patent application Ser. No. 12/020,450, “Systems and Methods for Identifying a Contra-ictal Condition in a Subject,” filed Jan. 25, 2008, the contents of which are hereby incorporated by reference in their entirety. The signals may also be stored and processed offline to, e.g., train customized algorithms for estimating the likelihood that a patient will experience an imminent seizure. See, e.g., U.S. Pat. No. 6,678,548, “Unified probabilistic framework for predicting and detecting seizure onsets in the brain and multitherapeutic device,” filed Oct. 20, 2000, the contents of which are hereby incorporated by reference in their entirety.
Other applications of multi-channel signal processing include the reception of wireless signals by a communications device using multiple antennas, geological monitoring of seismic activity for earthquake prediction, stereo imaging using multiple video cameras, etc.
When multi-channel signals are sampled over an extended period of time, artifacts or anomalies often appear in the signal. Such anomalies may be due to interference from external sources, disruptions to the power supply of the sensors, and/or other sources. Left untreated, such anomalies may degrade the quality of the measured signal and disrupt the accuracy of any subsequent processing of the multi-channel signal.
It would be desirable to have techniques to detect the presence of anomalies in a multi-channel signal, and to optimize the processing of a signal containing such anomalies.